SU‐FF‐P‐05: Five Year‐Report On a Web‐Based Interactive Dosimetry Training Tool
Bibliographic record
Abstract
Purpose: To address the critical shortage of medical dosimetrists in radiation oncology, an NCI‐funded web‐based ‘Dosimetry Training Tool’ education program was developed. The aim of this work was to present the end of funding period findings. Method and Materials: A dedicated group of volunteer medical physics and medical dosimetry experts developed high quality educational material enhanced by the interactive capabilities offered through web‐based learning. Educational rigor was provided by frequent interaction between the developers and a psychometrician and internet education specialist. During the development of the program, tests were performed to evaluate and revise the program direction using selected sites. Pre‐ and post module quizzes are taken by the students to quantify the educational benefit of the learning tool. An online user feedback tool is available for content and/or quiz queries. Results: Twenty‐four modules were developed, from Fundamentals of the Medical Management of Cancer (module 1), external beam and brachytherapy treatment planning, to Basic Math Skills for Dosimetry (module 24). Each module contains between 3 and 26 sessions. Over 1100 users, including over 800 students have used the dosimetry training tool, with a steady increase in the number of users throughout the program development. Mentors and students are predominantly from the US, however, other countries with approved mentors and active students are Afghanistan, Australia, Canada, China, Greenland, Hong Kong, Ireland, Israel, Pakistan, Singapore, Taiwan, and Thailand. Increases in pre‐ and post‐quiz scores range from 2 to 35% with a mean of 15% improvement. Conclusion: A successful web‐based dosimetry training tool has been developed and is in use by over 800 students. The use of the developed web‐based interactive educational design approach could be extended to augment and facilitate the development of medical physics graduate programs, medical physics residencies and other allied health care professional education.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".